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| 1 | +"""Tests for the multi-rank null-adapter calibration path (S10 / F4).""" |
| 2 | + |
| 3 | +from __future__ import annotations |
| 4 | + |
| 5 | +import math |
| 6 | + |
| 7 | +import numpy as np |
| 8 | +import pytest |
| 9 | + |
| 10 | +from dlm_sway.backends.dummy import DummyDifferentialBackend, DummyResponses |
| 11 | +from dlm_sway.core.result import Verdict |
| 12 | +from dlm_sway.probes._zscore import format_z_profile, z_scores_by_rank |
| 13 | +from dlm_sway.probes.base import RunContext, build_probe |
| 14 | +from dlm_sway.suite.runner import run as run_suite |
| 15 | +from dlm_sway.suite.spec import SwaySpec |
| 16 | + |
| 17 | + |
| 18 | +def _backend() -> DummyDifferentialBackend: |
| 19 | + return DummyDifferentialBackend(base=DummyResponses(), ft=DummyResponses()) |
| 20 | + |
| 21 | + |
| 22 | +class TestDummyBackendRankScale: |
| 23 | + def test_rank_scale_scales_noise_std(self) -> None: |
| 24 | + """sqrt(rank_scale) scales the null-view perturbation std.""" |
| 25 | + backend = _backend() |
| 26 | + prompt = "hello" |
| 27 | + |
| 28 | + # Collect 40 samples at each rank_scale to estimate std. |
| 29 | + def _std_at(rank_scale: float) -> float: |
| 30 | + lps = [] |
| 31 | + for seed in range(40): |
| 32 | + with backend.as_null_adapter(seed=seed, rank_scale=rank_scale) as view: |
| 33 | + d = view.next_token_dist(prompt, top_k=8) |
| 34 | + lps.append(d.logprobs) |
| 35 | + arr = np.asarray(lps) |
| 36 | + # Variance across seeds at each position, averaged. |
| 37 | + return float(np.mean(np.std(arr, axis=0))) |
| 38 | + |
| 39 | + std_1 = _std_at(1.0) |
| 40 | + std_half = _std_at(0.5) |
| 41 | + std_2 = _std_at(2.0) |
| 42 | + # std ∝ sqrt(rank_scale). Tolerance loose because of the |
| 43 | + # top-k renorm and seed discretion. |
| 44 | + assert std_half < std_1 < std_2 |
| 45 | + # Ratio should be roughly sqrt(2) with 20% tolerance. |
| 46 | + ratio_up = std_2 / std_1 |
| 47 | + ratio_down = std_1 / std_half |
| 48 | + assert 1.15 < ratio_up < 1.75, f"2x ratio={ratio_up}" |
| 49 | + assert 1.15 < ratio_down < 1.75, f"0.5x ratio={ratio_down}" |
| 50 | + |
| 51 | + @pytest.mark.parametrize("bad", [0.0, -1.0, float("nan"), float("inf")]) |
| 52 | + def test_rejects_non_positive_rank_scale(self, bad: float) -> None: |
| 53 | + backend = _backend() |
| 54 | + with pytest.raises(ValueError, match="rank_scale"): |
| 55 | + with backend.as_null_adapter(seed=0, rank_scale=bad): |
| 56 | + pass |
| 57 | + |
| 58 | + def test_rank_scale_1_preserves_pre_s10_behavior(self) -> None: |
| 59 | + """rank_scale=1.0 → identical output to calling without the kwarg.""" |
| 60 | + backend = _backend() |
| 61 | + with backend.as_null_adapter(seed=7) as v1: |
| 62 | + d1 = v1.next_token_dist("hello", top_k=8) |
| 63 | + with backend.as_null_adapter(seed=7, rank_scale=1.0) as v2: |
| 64 | + d2 = v2.next_token_dist("hello", top_k=8) |
| 65 | + np.testing.assert_array_equal(d1.logprobs, d2.logprobs) |
| 66 | + |
| 67 | + |
| 68 | +class TestNullProbeMultiRank: |
| 69 | + def test_single_rank_default_matches_pre_s10(self) -> None: |
| 70 | + """Default rank_multipliers=[1.0] produces the same shape of |
| 71 | + evidence as pre-S10 + a null_stats_by_rank with one entry.""" |
| 72 | + backend = _backend() |
| 73 | + probe, spec = build_probe( |
| 74 | + { |
| 75 | + "name": "null", |
| 76 | + "kind": "null_adapter", |
| 77 | + "runs": 3, |
| 78 | + "calibrate_kinds": ["delta_kl"], |
| 79 | + } |
| 80 | + ) |
| 81 | + ctx = RunContext(backend=backend) |
| 82 | + result = probe.run(spec, ctx) |
| 83 | + assert result.verdict == Verdict.PASS |
| 84 | + stats = result.evidence["null_stats"] |
| 85 | + by_rank = result.evidence["null_stats_by_rank"] |
| 86 | + assert "delta_kl" in stats |
| 87 | + assert set(by_rank) == {"rank_1.00"} |
| 88 | + assert by_rank["rank_1.00"] == stats |
| 89 | + |
| 90 | + def test_three_ranks_produce_three_groups(self) -> None: |
| 91 | + backend = _backend() |
| 92 | + probe, spec = build_probe( |
| 93 | + { |
| 94 | + "name": "null", |
| 95 | + "kind": "null_adapter", |
| 96 | + "runs": 3, |
| 97 | + "rank_multipliers": [0.5, 1.0, 2.0], |
| 98 | + "calibrate_kinds": ["delta_kl"], |
| 99 | + } |
| 100 | + ) |
| 101 | + ctx = RunContext(backend=backend) |
| 102 | + result = probe.run(spec, ctx) |
| 103 | + assert result.verdict == Verdict.PASS |
| 104 | + by_rank = result.evidence["null_stats_by_rank"] |
| 105 | + assert set(by_rank) == {"rank_0.50", "rank_1.00", "rank_2.00"} |
| 106 | + for rkey, kind_stats in by_rank.items(): |
| 107 | + assert "delta_kl" in kind_stats, f"{rkey} missing delta_kl" |
| 108 | + assert kind_stats["delta_kl"]["std"] > 0.0 |
| 109 | + |
| 110 | + def test_rank_0_and_negative_rejected(self) -> None: |
| 111 | + backend = _backend() |
| 112 | + probe, spec = build_probe( |
| 113 | + { |
| 114 | + "name": "null", |
| 115 | + "kind": "null_adapter", |
| 116 | + "runs": 2, |
| 117 | + "rank_multipliers": [1.0, -0.5], |
| 118 | + "calibrate_kinds": ["delta_kl"], |
| 119 | + } |
| 120 | + ) |
| 121 | + ctx = RunContext(backend=backend) |
| 122 | + result = probe.run(spec, ctx) |
| 123 | + assert result.verdict == Verdict.ERROR |
| 124 | + assert "rank_multipliers" in (result.message or "") |
| 125 | + |
| 126 | + def test_higher_rank_has_larger_null_std(self) -> None: |
| 127 | + """A 2x rank null should show more delta_kl variance than a 0.5x one.""" |
| 128 | + backend = _backend() |
| 129 | + probe, spec = build_probe( |
| 130 | + { |
| 131 | + "name": "null", |
| 132 | + "kind": "null_adapter", |
| 133 | + "runs": 5, |
| 134 | + "rank_multipliers": [0.5, 2.0], |
| 135 | + "calibrate_kinds": ["delta_kl"], |
| 136 | + "cache": False, |
| 137 | + } |
| 138 | + ) |
| 139 | + ctx = RunContext(backend=backend) |
| 140 | + result = probe.run(spec, ctx) |
| 141 | + by_rank = result.evidence["null_stats_by_rank"] |
| 142 | + std_half = by_rank["rank_0.50"]["delta_kl"]["std"] |
| 143 | + std_2 = by_rank["rank_2.00"]["delta_kl"]["std"] |
| 144 | + assert std_2 > std_half, f"2x std={std_2} not > 0.5x std={std_half}" |
| 145 | + |
| 146 | + |
| 147 | +class TestRunnerThreadsNullStatsByRank: |
| 148 | + def test_delta_kl_emits_z_by_rank(self) -> None: |
| 149 | + """null_adapter → delta_kl: evidence carries z_by_rank with three entries.""" |
| 150 | + backend = _backend() |
| 151 | + raw_spec = SwaySpec.model_validate( |
| 152 | + { |
| 153 | + "version": 1, |
| 154 | + "models": { |
| 155 | + "base": {"base": "b"}, |
| 156 | + "ft": {"base": "b", "adapter": "/tmp/a"}, |
| 157 | + }, |
| 158 | + "suite": [ |
| 159 | + { |
| 160 | + "name": "null", |
| 161 | + "kind": "null_adapter", |
| 162 | + "runs": 3, |
| 163 | + "rank_multipliers": [0.5, 1.0, 2.0], |
| 164 | + "cache": False, |
| 165 | + }, |
| 166 | + { |
| 167 | + "name": "dk", |
| 168 | + "kind": "delta_kl", |
| 169 | + "prompts": ["p1", "p2"], |
| 170 | + "assert_z_gte": -100.0, # permissive |
| 171 | + }, |
| 172 | + ], |
| 173 | + } |
| 174 | + ) |
| 175 | + result = run_suite(raw_spec, backend) |
| 176 | + assert len(result.probes) == 2 |
| 177 | + dk = result.probes[1] |
| 178 | + z_by_rank = dk.evidence.get("z_by_rank") |
| 179 | + assert z_by_rank is not None |
| 180 | + assert set(z_by_rank) == {"rank_0.50", "rank_1.00", "rank_2.00"} |
| 181 | + # Each z is finite. |
| 182 | + for z in z_by_rank.values(): |
| 183 | + assert math.isfinite(z) |
| 184 | + |
| 185 | + |
| 186 | +class TestZScoreHelpers: |
| 187 | + def test_z_scores_by_rank_positive_sign(self) -> None: |
| 188 | + raw = 1.0 |
| 189 | + stats_by_rank = { |
| 190 | + "rank_1.00": {"mean": 0.5, "std": 0.1, "n": 3.0}, |
| 191 | + "rank_0.50": {"mean": 0.3, "std": 0.1, "n": 3.0}, |
| 192 | + } |
| 193 | + z = z_scores_by_rank(raw, stats_by_rank, sign=+1) |
| 194 | + assert z is not None |
| 195 | + assert abs(z["rank_1.00"] - 5.0) < 1e-9 |
| 196 | + assert abs(z["rank_0.50"] - 7.0) < 1e-9 |
| 197 | + |
| 198 | + def test_z_scores_by_rank_negative_sign(self) -> None: |
| 199 | + """Lower-is-better probes invert the sign.""" |
| 200 | + raw = 0.1 |
| 201 | + stats_by_rank = {"rank_1.00": {"mean": 0.5, "std": 0.1, "n": 3.0}} |
| 202 | + z = z_scores_by_rank(raw, stats_by_rank, sign=-1) |
| 203 | + assert z is not None |
| 204 | + assert abs(z["rank_1.00"] - 4.0) < 1e-9 # -((0.1 - 0.5)/0.1) = 4 |
| 205 | + |
| 206 | + def test_z_scores_by_rank_none_on_empty(self) -> None: |
| 207 | + assert z_scores_by_rank(0.0, None) is None |
| 208 | + assert z_scores_by_rank(0.0, {}) is None |
| 209 | + |
| 210 | + def test_z_scores_by_rank_drops_degenerate_ranks(self) -> None: |
| 211 | + """Ranks with std < MIN_STD silently drop out.""" |
| 212 | + stats_by_rank = { |
| 213 | + "rank_1.00": {"mean": 0.0, "std": 0.1, "n": 3.0}, |
| 214 | + "rank_0.50": {"mean": 0.0, "std": 1e-9, "n": 3.0}, # degenerate |
| 215 | + } |
| 216 | + z = z_scores_by_rank(1.0, stats_by_rank, sign=+1) |
| 217 | + assert z is not None |
| 218 | + assert set(z) == {"rank_1.00"} |
| 219 | + |
| 220 | + def test_format_z_profile_readable_labels(self) -> None: |
| 221 | + s = format_z_profile( |
| 222 | + {"rank_1.00": 4.2, "rank_0.50": 6.8, "rank_2.00": 2.1}, |
| 223 | + ) |
| 224 | + assert "+4.20σ @ 1x" in s |
| 225 | + assert "+6.80σ @ 0.5x" in s |
| 226 | + assert "+2.10σ @ 2x" in s |
| 227 | + assert " / " in s |
| 228 | + |
| 229 | + def test_format_z_profile_empty(self) -> None: |
| 230 | + assert format_z_profile(None) == "" |
| 231 | + assert format_z_profile({}) == "" |
| 232 | + |
| 233 | + |
| 234 | +class TestProveTheValueRankSaturation: |
| 235 | + """S10 prove-the-value (§F4): rank profile reveals adapter saturation. |
| 236 | + |
| 237 | + The dummy backend's null view injects noise scaled by |
| 238 | + ``sqrt(rank_scale)`` into ``next_token_dist``. That scales the |
| 239 | + null distribution of ``delta_kl``'s raw metric (mean JS divergence |
| 240 | + across prompts) so that smaller ``rank_scale`` → tighter null → |
| 241 | + larger z at the same adapter divergence. |
| 242 | + |
| 243 | + Test: hold the adapter fixed (ft responses that produce a known |
| 244 | + divergence from base), vary rank_scale across {0.5, 1.0, 2.0}, and |
| 245 | + assert z_0.5 > z_1 > z_2 — exactly the signature of a rank-sized |
| 246 | + adapter: stronger signal vs a smaller-rank null, weaker signal vs |
| 247 | + a larger-rank null. |
| 248 | + """ |
| 249 | + |
| 250 | + def test_rank_profile_monotone_in_inverse_rank(self) -> None: |
| 251 | + backend = _backend() |
| 252 | + raw_spec = SwaySpec.model_validate( |
| 253 | + { |
| 254 | + "version": 1, |
| 255 | + "models": { |
| 256 | + "base": {"base": "b"}, |
| 257 | + "ft": {"base": "b", "adapter": "/tmp/a"}, |
| 258 | + }, |
| 259 | + "suite": [ |
| 260 | + { |
| 261 | + "name": "null", |
| 262 | + "kind": "null_adapter", |
| 263 | + "runs": 5, |
| 264 | + "rank_multipliers": [0.5, 1.0, 2.0], |
| 265 | + "cache": False, |
| 266 | + }, |
| 267 | + { |
| 268 | + "name": "dk", |
| 269 | + "kind": "delta_kl", |
| 270 | + "prompts": ["p1", "p2", "p3", "p4"], |
| 271 | + "assert_z_gte": -100.0, # permissive |
| 272 | + }, |
| 273 | + ], |
| 274 | + } |
| 275 | + ) |
| 276 | + result = run_suite(raw_spec, backend) |
| 277 | + dk = result.probes[1] |
| 278 | + z_by_rank = dk.evidence["z_by_rank"] |
| 279 | + # Smaller rank → tighter null → larger (more positive) z. |
| 280 | + z_half = z_by_rank["rank_0.50"] |
| 281 | + z_1 = z_by_rank["rank_1.00"] |
| 282 | + z_2 = z_by_rank["rank_2.00"] |
| 283 | + assert z_half > z_1 > z_2, ( |
| 284 | + f"expected z monotone-decreasing in rank; got " |
| 285 | + f"0.5x={z_half:.2f}, 1x={z_1:.2f}, 2x={z_2:.2f}" |
| 286 | + ) |
